Example-based explanations
Example-based explanations can be used either to provide more insight into a data set or insight into the predictions of machine learning models. The explanations themselves are (a selection of) data instances, which assumes that data instances can be presented in a meaningful way. For example, a data instance with 1000 features is not going to provide meaningful insights for humans.
Insight into data can be gained by providing prototypical examples, i.e. data instances that are representative of the data. Model predictions can in turn be explained by referring to similar examples for which the same prediction is made.
Counterfactual and contrastive examples are special cases for which this taxonomy has separate entries. Adversarial examples are discussed under security .